Procesamiento de Señales e Imagenes

Ingeniería Biomédica

Ph.D. Pablo Eduardo Caicedo Rodríguez

2024-08-12

#| echo: false
#| eval: true
#| output: false
#| label: Loading R-Libraries
# install.packages(c("DiagrammeR", "reticulate", "kableExtra", "tidyverse", "knitr", "cowplot", "ggfx"))
library("DiagrammeR")
library("reticulate")
library("kableExtra")
library("tidyverse")
library("knitr")
library("cowplot")
library("ggfx")
knitr::opts_chunk$set(echo = FALSE)

def.chunk.hook <- knitr::knit_hooks$get("chunk")
knitr::knit_hooks$set(chunk = function(x, options) {
    x <- def.chunk.hook(x, options)
    ifelse(options$size != "normalsize", paste0("\n \\", options$size, "\n\n", x, "\n\n \\normalsize"), x)
})

Procesado de Señales e Imágenes Médicas - PSIM

Introduction

Data
Acquisition

Signal
conditioning

Feature
Extraction

Hypothesis
Testing

  • Data acquisition is to capture the signal and encode in a form suitable for computer processing.
  • Signal conditioning is to remove noise and artifacts from the signal.
  • Feature extraction is to extract relevant information from the signal.
  • Hypothesis testing is to test the hypothesis based on the extracted features.

Signal condtioning

Signal conditioning

data  = sio.loadmat(path_ecg+"/JS00001.mat")
print(type(data))
print(data.keys())
print(type(data['val']))
print(data['val'].shape)
<class 'dict'>
dict_keys(['val'])
<class 'numpy.ndarray'>
(12, 5000)

Signal conditioning